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Applying Devops Practices of Continuous Automation for Machine Learning information Article Applying DevOps Practices of Continuous Automation for Machine Learning Ioannis Karamitsos 1,* , Saeed Albarhami 1 and Charalampos Apostolopoulos 2 1 Department of Computing, Rochester Institute of Technology, Dubai Campus, Dubai 341055, UAE; [email protected] 2 Department of Management Science, University of Strathclyde Business School, Glasgow G1 1XQ, UK; [email protected] * Correspondence: [email protected] Received: 1 June 2020; Accepted: 5 July 2020; Published: 13 July 2020 Abstract: This paper proposes DevOps practices for machine learning application, integrating both the development and operation environment seamlessly. The machine learning processes of development and deployment during the experimentation phase may seem easy. However, if not carefully designed, deploying and using such models may lead to a complex, time-consuming approaches which may require significant and costly efforts for maintenance, improvement, and monitoring. This paper presents how to apply continuous integration (CI) and continuous delivery (CD) principles, practices, and tools so as to minimize waste, support rapid feedback loops, explore the hidden technical debt, improve value delivery and maintenance, and improve operational functions for real-world machine learning applications. Keywords: CRISP-DM; CI; CD; DevOps; machine learning; pipeline; SEMMA; TDSP 1. Introduction In the data science world, machine learning (ML) is becoming a core approach for solving complex real-world problems, transforming industries, and delivering value in respective domains. Therefore, there are many data science teams in the scientific and machine learning communities trying to improve the overall business value using descriptive and predictive models. As a consequence, ML data scientists and ML operations engineer teams study how to apply DevOps principles to their ML systems under study. DevOps [1] is a set of practices and tools based on software and systems engineering. Software engineering can be defined as a discipline dedicated to developing tools and techniques which allow creation and use of sophisticated software systems. Data science is less about program development and more about analyzing and getting insights from the data. Agile [2], on the other hand, refers to an iterative approach which focuses on collaboration, customer feedback, and small and rapid releases. DevOps and Agile are two pillars to support in achieving business strategy and overlap the traditional operational and developmental teams to create an environment that is continually improving operations through a cross-functional team of developers and operators. The DevOps strategic objective is to investigate methods for improving service quality and features in a manner that satisfies their customer needs (Farroha [3]). ML data scientists and ML operations engineering teams have introduced manual steps for the delivery of ML pipeline model. This method may likely produce unexpected results due to the dependency on data collection, preparation and preprocessing, model training, validation, and testing. Moreover, this method led to the conclusion that no apparent advantage exists in utilizing our manual approach for ML projects. Also, in terms of quality results, this ML manual pipeline method produces Information 2020, 11, 363; doi:10.3390/info11070363 www.mdpi.com/journal/information Information 2020, 11, 363 2 of 15 high operational costs and delays, which directly or indirectly affect the revenue or quality reputation of the business. We can conclude that there are four research questions associated with the above ML manual pipeline approach. RQ1: Which DevOps tools have been selected to design and implement automate • deployment pipelines? RQ2: What challenges have been reported or adopting DevOps continuous practices? • RQ3: How does DevOps impact the ML manual pipeline method in terms of performance, • scalability, and monitoring? RQ4: How can we find a new approach for the ML manual pipeline method to improve the ML • tracking lifecycle? To address these research questions, a novel approach is proposed in this paper to introducing the two DevOps principles in the MLOps approach. The main idea is to design a machine learning automate pipeline using two DevOps [1] principles: the continuous integration (CI) and the continuous delivery (CD). Practicing MLOps means that we advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management. This specific operation of ML complex systems incorporates two important DevOps principles [1], continuous delivery (CD) and continuous integration (CI). The functionality of CI is not limited only for testing, and validating code and components, but also testing and validating data, data schemas, and models. CD is no longer about a single package or service, but an ML pipeline that should automatically deploy another ML service. The research method used in this study is the systematic literature review (SLR) [4] as the most widely used in the software engineering area. SLR aims to provide a well-defined process for identifying, evaluating, and interpreting all available evidence relevant to a particular research questions or topic. Following the SLR guidelines reported in [4], our review protocol consisted of (i) research questions, (ii) study selection, (iii) assignment criteria, and (v) data extraction and synthesis. The target audience of this paper is ML data scientists who want to apply the ML trained models using CI and container principles in the testing environment, as well as ML operations engineers working on tested ML models in the production environment. The paper makes the following contributions: a review of the two principal DevOps components—continuous integration (CI) and continuous • delivery (CD)—in the ML context, a ML manual pipeline design with the components in details, • a ML automate pipeline design with CI/CD components in details • The rest of the paper is organized as follows. Section2 includes the background information of DevOps and the related works on different DevOps and ML scenarios. In Section3, we present different ML lifecycle methodologies. In Section4, we design the proposed ML pipeline manually and ML with CI/CD automation. Section5 presents ML scalability for di fferent pipelines. Finally, Section6 concludes the paper. 2. Background In this section, we provide an overview of DevOps principles as part of continuous software engineering paradigm. 2.1. Continuous Software Engineering Continuous software engineering is an emerging area of research and practice. It refers to development, deployment, and getting quick feedback from software and customers in a very rapid Information 2020, 11, 363 3 of 15 cycle [5,6]. Continuous software engineering involves three phases: business strategy and planning, development, and operation with the following software development activities: continuous integration and continuous delivery. Continuous integration (CI) is a widely established development practice in software development industry [5], in which members of a team integrate and merge development work (e.g., code) frequently, for example multiple times per day. CI enables software companies to have shorter and frequent release cycle, improve software quality, and increase their teams’ productivity [6]. This practice includes automated software building and testing [7]. Continuous delivery (CD) is aimed at ensuring an application is always at production-ready state after successfully passing automated tests and quality checks [8,9]. CD employs a set of practices e.g., CI, and deployment automation to deliver software automatically to a production-like environment [10]. CD is a push-based approach [11]. According to [11,12], this practice offers several benefits such as reduced deployment risk, lower costs, and getting user feedback faster. 2.2. DevOps DevOps [1] stands for development and operations. It’s a practice that aims at merging development, quality assurance, and operations (deployment and integration) into a single, continuous set of processes. This methodology is a natural extension for Agile and continuous delivery approaches from the software engineering paradigm. However, DevOps is not merely a set of actions. It is more of a culture or even a philosophy that fosters cross-functional team communication. One of the main benefits of DevOps is that it does not require substantial technical changes being rather oriented to changing the way a team works. Teamwork is a crucial part of DevOps culture: the whole success of a process depends on it, and there are principles and practices that DevOps teams use. 2.2.1. DevOps Principles In short, the main principles of DevOps are automation, continuous delivery, and fast reaction to feedback. You can find a more detailed explanation of DevOps pillars in the CAMS acronym [1]: Culture represented by human communication, technical processes, and tools Automation of processes Measurement of KPIs Sharing feedback, best practices, and knowledge Adherence to these principles is achieved through a number of DevOps practices that include continuous delivery, frequent deployments, QA automation, validating ideas as early as possible, and in-team collaboration. 2.2.2. DevOps Model and Practices DevOps [1] requires a delivery cycle that comprises planning, development, testing, deployment, release, and monitoring with active cooperation between
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